A New Global Grid-Based Weighted Mean Temperature Model Considering Vertical Nonlinear Variation

Global navigation satellite systems (GNSS) have been proved to be an excellent technology for retrieving precipitable water vapor (PWV). In GNSS meteorology, PWV at a station is obtained from a conversion of the zenith wet delay (ZWD) of GNSS signals received at the station using a conversion factor which is a function of weighted mean temperature (T-m) along the vertical direction in the atmosphere over the site. Thus, the accuracy of T-m directly affects the quality of the GNSS-derived PWV. Currently, the T-m value at a target height level is commonly modeled using the T-m value at a specific height and a simple linear decay function, whilst the vertical nonlinear variation in T-m is neglected. This may result in large errors in the T-m result for the target height level, as the variation trend in the vertical direction of T-m may not be linear. In this research, a new global grid-based T-m empirical model with a horizontal resolution of 1 degrees x 1 degrees , named GGNTm, was constructed using ECMWF ERAS monthly mean reanalysis data over the 10-year period from 2008 to 2017. A three-order polynomial function was utilized to fit the vertical nonlinear variation in T-m at the grid points, and the temporal variation in each of the four coefficients in the T-m fitting function was also modeled with the variables of the mean, annual, and semiannual amplitudes of the 10-year time series coefficients. The performance of the new model was evaluated using its predicted T-m values in 2018 to compare with the following two references in the same year: (1) T-m from ERAS hourly reanalysis with the horizontal resolution of 5 degrees x 5 degrees, (2) T-m from atmospheric profiles from 428 globally distributed radiosonde stations. Compared to the first reference, the mean RMSEs of the model-predicted T-m values over all global grid points at the 950 and 500 hPa pressure levels were 3.35 and 3.94 K, respectively. Compared to the second reference, the mean bias and mean RMSE of the model-predicted T-m values over the 428 radiosonde stations at the surface level were 0.34 and 3.89 K, respectively; the mean bias and mean RMSE of the model's T-m values over all pressure levels in the height range from the surface to 10 km altitude were -0.16 and 4.20 K, respectively. The new model results were also compared with that of the GTrop and GWMT_D models in which different height correction methods were also applied. Results indicated that significant improvements made by the new model were at high-altitude pressure levels; in all five height ranges, GGNTm results were generally unbiased, and their accuracy varied little with height. The improvement in PWV brought by GGNTm was also evaluated. These results suggest that considering the vertical nonlinear variation in T-m and the temporal variation in the coefficients of the T-m model can significantly improve the accuracy of model-predicted T-m for a GNSS receiver that is located anywhere below the tropopause (assumed to be 10 km), which has significance for applications requiring real-time or near real-time PWV converted from GNSS signals.